One problem of entering text into devices having small form factors (like cellular phones, personal digital assistants (PDAs), and others like the RIM-Blackberry and Apple-iPod) has existed for a while now. This problem is of specific importance because these devices, given their form-factors, lack a full-size keyboard (found in desktop computers or laptops) and hence inputting text using the alternative input methods is painful and slow. Text Input technologies have many practical mobile applications that include text-messaging (short messaging service or SMS, multimedia messaging service or MMS, email, instant messaging or IM), wireless internet browsing, wireless content search, and mobile personal information management. A brief background on text input technologies is provided next.
Text prediction methods may be broadly classified into three categories: (a) prediction based on unambiguous partial-spelling of the text, also referred to as word-completion, (b) prediction based on ambiguous partial-spelling of text, and (c) prediction based on ambiguous full-spelling of text.
Prediction based on unambiguous partial-spelling of the text: Word completion techniques are typically employed on devices which have some form of a keyboard (examples include mobile devices with a mini-qwerty keyboard as in Blackberry; word-editors running on personal computers, laptops and tablet PCs; personal digital assistants with soft-keys and/or hand-writing recognition software). In these devices, the presence of individual distinct buttons for all characters implies unambiguous input of letters. Obviously, the word completion techniques attempt to predict the word based on partially entered letters corresponding to the spelling of the text; thereby attempting to increase the overall speed of text input.
Prediction based on ambiguous full-spelling of text: Companies like Tegic-T9, Motorola-iTap, ZiCorp-EZiText, Eatoni, and others have introduced variants of predictive text input process for the 9-digit telephone keypad. These methods use the initial typed letters (which are ambiguous because a single key-press may imply any of 3-4 letters) to generate a possible combination sequence, followed by a search against a lexicon of words, to predict the word; which is then displayed onto the mobile-device screen. For instance, using the Tegic-T9 method, a user keys in the characters “C” and “A” (by tapping keys “2” and “2” respectively), and subsequently the process may predict the word “CALL” (although “BALL” may very well be an alternative). Another example is when a user attempts to dial a number using contacts on a mobile device, the user presses the buttons “5” and “6” and the underlying process predicts the name “JOHN”. These predictive-text-input methods have proven to increase the speed of text-input compared to standard multi-tap based methods.
Prediction based on ambiguous partial-spelling of text: Tegic-T9 recently introduced an XT9 enhancement which attempts to predict text (a word and/or even the next word) based on ambiguous partial-spelling of that text. This may be viewed as a combination of ambiguous full-spelling based prediction and unambiguous partial-spelling based prediction described above. In addition to standard 9-digit keypad based devices (which are inherently ambiguous inputs because of lack of individual keys for distinct letters) this technology also addresses scenarios wherein users mistype keys on devices having distinct keys. The keys, although distinct, are very small and hence it is likely that a wrong neighboring key gets pressed giving rise to ambiguous inputs.
Speech-to-Text systems attempt to convert spoken text into text with the hope that a user could simply speak an entire sentence and the device could type the same. This technology, also referred to as “continuous speech recognition”, has made several advances over the past five decades. Unfortunately, its performance is not satisfactory for wide commercial usage.